Geography

• What is your favourite sport?
• Do you speak Spanish?
• Do you know who Fofão is?
• How many kisses on the cheek do you greet someone with?
• If you are on your own in a taxi do you sit in the front or back?
• Do you think government policy should allow free migration?
• Where do you live?

Geography

• Knowledge and communication depend on where we live

• Social norms and customs depend on where we live

• Political preferences depend on where we live

Geography

Tobler’s First Law of Geography:

“Everything is related to everything else, but near things are more related than distant things”

Geography

• What does ‘near’ mean?

• Concepts of distance:
  • Euclidean
  • Great Circle
  • Manhattan
  • Levensthein
  • Mahalanobis
  • Driving
  • Network
  • Minimum-cost
  • Genetics

Geography

• What does ‘related’ mean?
  • Correlated
  • More similar
  • More different (ex. dialing codes to avoid typing errors)

• ‘Related’ does not mean one person ‘causes’ a similar effect on another
  • It may just be a common response to a similar environment
  • But interactions and spillovers are common

Geography

• Locations of ‘Events’ could be ‘near’ to each other

Geography

• Or characteristics of locations could be ‘near’ to each other

Geography

• Multiple characteristics could also be ‘near’ to each other

Geography

Geography

Geography

• But isn’t the world getting smaller?
  • ‘The death of distance’
  • Everything is ‘near’ on the internet
• Relevant distances may be changing
  • Cost of flights instead of kilometres or hours
  • Language and social network instead of proximity to radio tower
• Spatial relationships take place at multiple scales
  • I am Welsh, British, European etc.
  • The similarities between rural China and rural Russia are greater than the differences

Geography

• Lots of interesting questions are really non-spatial
  • We can draw maps of them
  • But the conclusion does not depend on the locations of the units

Geography

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• Geography is more than just ‘clustering’

• A Typology of Spatial Relationships
  1. Clustering
  2. Natural resources/barriers
  3. Administrative barriers

Geography

• Physical features also affect social and political processes
  • Attracting economic activity
  • Preventing interactions

Geography

Geography

Geography

Merits of Spatial Analysis

Merits of Spatial Analysis

Map Literacy

• Maps are clear and convincing
  • Patterns may only be visible when arranged spatially
  • If you have spatial data, why put it in a table or a chart?

Map Literacy

Map Literacy

Map Literacy

Map Literacy

• But maps still require careful interpretation

Map Literacy

• Scale
  • Can I walk from The Art Institute of Chicago to Union Station in 10 minutes?

Map Literacy

• Scale
  • Can I walk from The Art Institute of Chicago to Union Station in 10 minutes?

Map Literacy

• Compass
  • What’s the best place to view the sunset in the Wirral (UK)?

Map Literacy

• Compass
  • What’s the best place to view the sunset in the Wirral (UK)?

Map Literacy

• Legend
• Can be manipulated to convey relevant (or misleading!) conclusions

Map Literacy

• Choosing the Indicator
  • The most important! What precisley do we want to convey?

Map Literacy

• Choosing the Indicator
  • The most important! What precisley do we want to convey?

Map Literacy

• Mapping values to colours

Map Literacy

• Mapping values to colours
  • Hard: Chosing break points between categories
  • Equal intervals, quantiles, standard deviations, ‘natural’ breaks

Map Literacy

• Mapping values to colours
  • Hard: Chosing break points between categories
  • Equal intervals, quantiles, standard deviations, ‘natural’ breaks

Map Literacy

• Mapping values to colours

Map Literacy

• Mapping values to colours

Map Literacy

• Mapping values to colours

Geographic Information Systems

1. Convert the real world into a digital model
   • Necessarily simplified

2. Compare multiple spatial layers

3. Create measures and statistics to describe spatial relationships

Vector vs. Raster Data

Vector vs. Raster Data

Types of Vector Data

• An analysis choice, and depends on scale

Types of Vector Data

• The attributes we assign to vector objects also vary

Locations in Space

• How do we describe the location of an object in space?

• The real world is 3-dimensional
  • Mostly we deal with points on the earth’s surface
  • This is not a problem for computers that can create ‘virtual earths’
• Geographic Coordinate Systems
  • ‘Perfect’ representations of earth in the computer
  • Longitude and Latitude define any point on earth

Locations in Space

• Longitude = Angle from equator (N/S)
• Latitude = Angle from Greenwich, London (E/W)

Locations in Space

• Longitude & Latitude can be measured in different units
  • DMS: 49°30’00″N, 123°30’00″W
  • DM: 49°30.0′, -123°30.0’
  • Decimal Degrees: 49.5000°,-123.5000°
• But all of these use the same Geographic Coordinate System
  • And we ‘always’ use the same one
  • WGS-84

Locations in Space

• What shape is the earth?
  • An ‘oblate spheroid’

• This oblate spheroid is estimated by a ‘datum’ so we get the location correct
  • No need to worry about this, WGS-84 includes its own datum

Locations in Space

• But we view maps on flat surfaces: paper or screens
  • Try peeling an orange
• To produce flat maps we need a Projected Coordinate Reference System
  • Translating 3-D locations to 2-D locations
  • There are many different ways to do this, just as there are many ways to peel an orange

Locations in Space

• Projections can preserve shape, area or distance, but not all three!

Locations in Space

• Projections are less distorted if they are localized to one part of the earth
  • So we choose a projection based on the extent of our analysis/map

• Ex. UTM (Universal Transverse Mercator) Zone

• Use http://epsg.io/ to find appropriate local projections

Locations in Space

Locations in Space

Coordinate Reference Systems have useful shortcut EPSG codes - In R, this is all you need

Locations in Space

• Which Coordinate Reference System (CRS) should I use?
  • Important: You don’t choose - your data sources already come with a specific CRS
  • Important: ALL data in the analysis must use the same CRS
  • That means sometimes we have to transform from one coordinate system to another
  • For projections, do you want to convey shape, area or distance accurately?
• For distance, what units do you want to use?
  • Geographic: Degrees
  • Projected: Meters (usually)

Georeferencing

• With a CRS, computers understand locations such as -23.562778, -46.725261
• But what if we have a street address?
  • Ex. Av. Paulista, 1578, Sao Paulo
  • We can use georeferencing databases to return the coordinates
  • Ex. https://nominatim.openstreetmap.org

Georeferencing

• We can also take an image and georeference it to a map

• We need to ‘pin’ the map to at least two points
  • And ‘wrap’ it around the earth’s surface
  • Ex. https://www.georeferencer.com/

Spatial Datasets

• Non-spatial datasets are just tables

• Spatial datasets just add location data to the table

Spatial Datasets

Spatial Datasets

• Vector Spatial Datasets
  • Coordinates for every object
  • Multiple coordinates for lines, polygons

Spatial Datasets

• Vector Spatial Datasets
  • Coordinates for every object
  • Multiple coordinates for lines, polygons

Spatial Datasets

• One single ‘Multipolygon’ can be complicated
  • Comprised of many distinct polygons
  • Polygons can have ‘holes’ in them

Spatial Datasets

• Raster Spatial Datasets
  • Coordinates for every data point

Spatial Datasets

• Historically, vector data has been stored as shapefiles
  • Shapefiles separate out the tables, location data, projection into separate files

Spatial Datasets

• Raster data is typically stored as .tiff files
  • The same as you get from a camera or scanner
  • But with location and projection data so that we know ‘where’ the image corresponds to

Non-Spatial Joins

• Most of our data is non-spatial, but could be made spatial
  • Election results
  • Death rates
  • Welfare payments
  • Conflict
• We can make this data spatial if we link it to existing spatial (location) data
  • Using common identifiers in both datasets
  • Non-spatial joins

Non-Spatial Joins

• Governments publish school performance data
  • But what is the spatial pattern of school performance?
  • Better in the city centre or in the suburbs?
• We need a source for the location of the schools
  • Perhaps from a separate geographical survey
  • Or by georeferencing their addresses
• How do we combine the school performance and location datasets?
  • By code
  • By name?

Non-Spatial Joins

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• examples of types of spatial analysis